Tuesday, October 29, 2013

Vicarious Has a Winner

Vicarious Wakes up with a Bang

It has been a while since we heard anything from Vicarious. I was beginning to wonder if the company had fallen asleep. Then suddenly out of nowhere, they announced that they have a machine learning program that can solve CAPTCHAs, the sometimes hard to read letter puzzles that are meant to ward off those pesky computer bots. That's a rather dramatic awakening, I would say. Although I do not agree with Vicarious's Bayesian or probabilistic approach to AI, I have to admit that this is very impressive.

A Few Observations

There are a few things about this new development that intrigue me. First of all, why didn't Vicarious host an online demo somewhere in the cloud and release a free app that others can use to test their claim? How hard would that be? It would have added some meat to the sauce, so to speak. Second, and this is more a question than a criticism, my understanding is that the recursive cortical network (RCN) works best with moving pictures. It is hard to imagine how it learns using static pictures. Third, Vicarious's CEO, D. Scott Phoenix, claimed that RCN needs less than 10 training examples per letter whereas other visual recognition programs require thousands of examples. This is truly amazing and, if true, it tells me that they must have figured out an efficient way to do invariant pattern recognition.

Why I Still Don't Think Vicarious Is on the Right Track

Yes, I still think that the Bayesian approach to AI is a red herring. Vicarious's CTO and co-founder, Dileep George, is convinced that intelligence is based on probabilistic math. I believe that neither human nor animal intelligence uses probability for reasoning, prediction or planning. We are cause/effect thinkers, not probability thinkers. The brain has a fast and effective way of compensating for the uncertain or probabilistic nature of the sensory stream by filling in any missing information and filtering out the noise. I see essentially two competing models. The Bayesian model assumes that the world is inherently uncertain and that the job of an intelligent system is to calculate the probabilities. The Rebel Science model, by contrast, assumes that the world is perfect and that the job of the intelligent system is to discover this perfection.

In Secrets of the Holy Grail, I wrote, "nobody can rightfully claim to understand the brain’s perceptual learning mechanism without also knowing exactly what the brain does during sleep and why." I'll say it again. If the guys at Vicarious don't know why the brain's neural network needs sleep, then they are not doing it right.